OpenRaiser/Intern-Atlas huggingface dataset:
MagicWand: A Universal Agent for Generation and Evaluation Aligned with User Preference
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in AIGC (Artificial Intelligence Generated Content) models have enabled significant progress in image and video generation. However, users still struggle to obtain content that aligns with their preferences due to the difficulty of crafting detailed prompts and the lack of mechanisms to retain their preferences. To address these challenges, we construct \textbf{UniPrefer-100K}, a large-scale dataset comprising images, videos, and associated text that describes the styles users tend to prefer. Based on UniPrefer-100K, we propose \textbf{MagicWand}, a universal generation and evaluation agent that enhances prompts based on user preferences, leverages advanced generation models for high-quality content, and applies preference-aligned evaluation and refinement. In addition, we introduce \textbf{UniPreferBench}, the first large-scale benchmark with over 120K annotations for assessing user preference alignment across diverse AIGC tasks. Experiments on UniPreferBench demonstrate that MagicWand consistently generates content and evaluations that are well aligned with user preferences across a wide range of scenarios.
TRANSPORTER: Transferring Visual Semantics from VLM Manifolds
Venue: arXiv cs.CV 2025
Year: 2025

How do video understanding models acquire their answers? Although current Vision Language Models (VLMs) reason over complex scenes with diverse objects, action performances, and scene dynamics, understanding and controlling their internal processes remains an open challenge. Motivated by recent advancements in text-to-video (T2V) generative models, this paper introduces a logits-to-video (L2V) task alongside a model-independent approach, TRANSPORTER, to generate videos that capture the underlying rules behind VLMs' predictions. Given the high-visual-fidelity produced by T2V models, TRANSPORTER learns an optimal transport coupling to VLM's high-semantic embedding spaces. In turn, logit scores define embedding directions for conditional video generation. TRANSPORTER generates videos that reflect caption changes over diverse object attributes, action adverbs, and scene context. Quantitative and qualitative evaluations across VLMs demonstrate that L2V can provide a fidelity-rich, novel direction for model interpretability that has not been previously explored.
MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-driven reasoning involving motion dynamics and spatial interactions. This limitation reduces their ability to interpret real or AI-generated content (AIGC) videos and to generate physically consistent content. We present an approach that addresses this gap by translating physical-world context cues into interpretable representations aligned with VLMs' perception, comprehension, and reasoning. We introduce MASS-Bench, a comprehensive benchmark consisting of 4,350 real-world and AIGC videos and 8,361 free-form video question-answering pairs focused on physics-related comprehension tasks, with detailed annotations including visual detections, sub-segment grounding, and full-sequence 3D motion tracking of entities. We further present MASS, a model-agnostic method that injects spatial-temporal signals into the VLM language space via depth-based 3D encoding and visual grounding, coupled with a motion tracker for object dynamics. To strengthen cross-modal alignment and reasoning, we apply reinforcement fine-tuning. Experiments and ablations show that our refined VLMs outperform comparable and larger baselines, as well as prior state-of-the-art models, by 8.7% and 6.0%, achieving performance comparable to close-source SoTA VLMs such as Gemini-2.5-Flash on physics reasoning and comprehension. These results validate the effectiveness of our approach.
Explicit Bounds on the Hausdorff Distance for Truncated mRPI Sets via Norm-Dependent Contraction Rates
Venue: arXiv cs.RO 2025
Year: 2025

This paper establishes the first explicit and closed-form upper bound on the Hausdorff distance between the truncated minimal robust positively invariant (mRPI) set and its infinite-horizon limit. While existing mRPI approximations guarantee asymptotic convergence through geometric or norm-based arguments, none provides a computable expression that quantifies the truncation error for a given horizon. We show that the error satisfies \( d_H(\mathcal{E}_N,\mathcal{E}_\infty) \le r_W\,γ^{N+1}/(1-γ), \) where $γ<1$ is the induced-norm contraction factor and $r_W$ depends only on the disturbance set. The bound is fully analytic, requires no iterative set computations, and directly characterizes the decay rate of the truncated Minkowski series. We further demonstrate that the choice of vector norm serves as a design parameter that accelerates convergence, enabling substantially tighter horizon selection for robust invariant-set computations and tube-based MPC. Numerical experiments validate the sharpness, scalability, and practical relevance of the proposed bound.
On Addressing Isolation in Blockchain-Based Self-Sovereign Identity
Venue: arXiv cs.CR 2025
Year: 2025

Self-Sovereign Identity (SSI) grants holders full ownership and control of their digital identities, being the ultimate digital identity model. Operating in a decentralized manner, SSI enables the verification of claims, including privacy-preserving mechanisms. Blockchain, which can be used to implement a Verifiable Data Registry (VDR), is often considered one of the pillars of SSI, along with Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Unfortunately, blockchains are mostly siloed, affecting the interoperability and universality of SSI. We investigate the effect of blockchain isolation on blockchain-based SSI. We first define possible scenarios for cross-chain SSI and exemplify with real-life use cases. We then define specific requirements for cross-chain SSI and identify challenges, also in relation to the identified scenarios. We explore various solutions to achieve blockchain interoperability, with a focus on SSI. In particular, we identify the advantages and disadvantages of distinct cross-chain models for cross-chain SSI. Finally, we address the usability of cross-chain SSI and discuss security and privacy aspects, opening the way for future research.
RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models
Venue: arXiv cs.CV 2025
Year: 2025

Mamba has recently garnered attention as an effective backbone for vision tasks. However, its underlying mechanism in visual domains remains poorly understood. In this work, we systematically investigate Mamba's representational properties and make three primary contributions. First, we theoretically analyze Mamba's relationship to Softmax and Linear Attention, confirming that it can be viewed as a low-rank approximation of Softmax Attention and thereby bridging the representational gap between Softmax and Linear forms. Second, we introduce a novel binary segmentation metric for activation map evaluation, extending qualitative assessments to a quantitative measure that demonstrates Mamba's capacity to model long-range dependencies. Third, by leveraging DINO for self-supervised pretraining, we obtain clearer activation maps than those produced by standard supervised approaches, highlighting Mamba's potential for interpretability. Notably, our model also achieves a 78.5 percent linear probing accuracy on ImageNet, underscoring its strong performance. We hope this work can provide valuable insights for future investigations of Mamba-based vision architectures.
NSTR: Neural Spectral Transport Representation for Space-Varying Frequency Fields
Venue: arXiv cs.SD 2025
Year: 2025

Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing signals such as images, audio, and 3D scenes. However, existing INR frameworks -- including MLPs with Fourier features, SIREN, and multiresolution hash grids -- implicitly assume a \textit{global and stationary} spectral basis. This assumption is fundamentally misaligned with real-world signals whose frequency characteristics vary significantly across space, exhibiting local high-frequency textures, smooth regions, and frequency drift phenomena. We propose \textbf{Neural Spectral Transport Representation (NSTR)}, the first INR framework that \textbf{explicitly models a spatially varying local frequency field}. NSTR introduces a learnable \emph{frequency transport equation}, a PDE that governs how local spectral compositions evolve across space. Given a learnable local spectrum field $S(x)$ and a frequency transport network $F_θ$ enforcing $\nabla S(x) \approx F_θ(x, S(x))$, NSTR reconstructs signals by spatially modulating a compact set of global sinusoidal bases. This formulation enables strong local adaptivity and offers a new level of interpretability via visualizing frequency flows. Experiments on 2D image regression, audio reconstruction, and implicit 3D geometry show that NSTR achieves significantly better accuracy-parameter trade-offs than SIREN, Fourier-feature MLPs, and Instant-NGP. NSTR requires fewer global frequencies, converges faster, and naturally explains signal structure through spectral transport fields. We believe NSTR opens a new direction in INR research by introducing explicit modeling of space-varying spectrum.
A pipeline for matching bibliographic references with incomplete metadata: experiments with Crossref and OpenCitations
Venue: arXiv cs.DL 2025
Year: 2025

While Crossref makes available more than 1.8 billion bibliographic references from publications for which it provides a DOI, more than 698 million of these references do not specify a DOI, making the creation of a formal citation link from the citing entity and the cited entity problematic. In this article, we propose an analysis of Crossref bibliographic references to show how we can use the unstructured text defining such references and the available (and partial) metadata specified in them to (a) map them to existing entities included in OpenCitations Meta and, then, (b) to enable the potential inclusion of additional and valid citations link among these entities. We have defined a precise methodology to address the analysis and run it against a manually defined Gold Standard and a subset of Crossref. While the heuristic-based tool developed has demonstrated strong matching precision and effective metadata integration, its recall limitations highlight the necessity of further enhancements to address metadata inconsistencies and leverage additional sources of citation data.
ioPUF+: A PUF Based on I/O Pull-Up/Down Resistors for Secret Key Generation in IoT Nodes
Venue: arXiv cs.CR 2025
Year: 2025

In this work, we present ioPUF+, which incorporates a novel Physical Unclonable Function (PUF) that generates unique fingerprints for Integrated Circuits (ICs) and the IoT nodes encompassing them. The proposed PUF generates device-specific responses by measuring the pull-up and pull-down resistor values on the I/O pins of the ICs, which naturally vary across chips due to manufacturing-induced process variations. Since these resistors are already integrated into the I/O structures of most ICs, ioPUF+ requires no custom circuitry, and no new IC fabrication. This makes ioPUF+ suitable for cost-sensitive embedded systems built from Commercial Off-The-Shelf (COTS) components. Beyond introducing a new PUF, ioPUF+ includes a complete datapath for converting raw PUF responses into cryptographically usable secret keys using BCH error correction and SHA-256 hashing. Further ioPUF+ also demonstrate a practical use case of PUF derive secret keys in securing device-to-device communication using AES-encryption. We implemented ioPUF+ on the Infineon PSoC-5 microcontroller and evaluated its performance across 30 devices using standard PUF metrics. The results show excellent reliability (intra-device Hamming distance of 100.00%), strong uniqueness (inter-device Hamming distance of 50.33%), near-ideal uniformity (50.54%), and negligible bit aliasing. Stability tests under temperature and supply-voltage variations show worst-case bit-error rates of only 2.63% and 2.10%, respectively. We also profiled the resource and energy usage of the complete ioPUF+ system, including the PUF primitive, BCH decoding, SHA-256 hashing, and AES encryption. The full implementation requires only 19.8 KB of Flash, exhibits a latency of 600 ms, and consumes 79 mW of power, demonstrating the suitabilitiy of ioPUF+ for resource-constrained IoT nodes.
DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation
Venue: arXiv cs.SD 2025
Year: 2025

Audio classifiers frequently face domain shift, when models trained on one dataset lose accuracy on data recorded in acoustically different conditions. Previous Test-Time Adaptation (TTA) research in speech and sound analysis often evaluates models under fixed or mismatched noise settings, that fail to mimic real-world variability. To overcome these limitations, this paper presents DHAuDS (Dynamic and Heterogeneous Audio Domain Shift), a benchmark designed to assess TTA approaches under more realistic and diverse acoustic shifts. DHAuDS comprises four standardized benchmarks: UrbanSound8K-C, SpeechCommandsV2-C, VocalSound-C, and ReefSet-C, each constructed with dynamic corruption severity levels and heterogeneous noise types to simulate authentic audio degradation scenarios. The framework defines 14 evaluation criteria for each benchmark (8 for UrbanSound8K-C), resulting in 50 unrepeated criteria (124 experiments) that collectively enable fair, reproducible, and cross-domain comparison of TTA algorithms. Through the inclusion of dynamic and mixed-domain noise settings, DHAuDS offers a consistent and publicly reproducible testbed to support ongoing studies in robust and adaptive audio modeling.
A General (Uniform) Relational Semantics for Sentential Logics
Venue: arXiv cs.LO 2025
Year: 2025

We present a general relational semantics framework which, by varying the axiomatization and components of the relational structures, provides a uniform semantics for sentential logics, classical and non-classical alike. The approach we take rests on a generalization of the Jónsson-Tarski representation (and duality) for Boolean algebras with operators to the cases of posets, semilattices, or bounded lattices (with, or without distribution) with quasi-operators. Completeness proofs rely on a choice-free construction of canonical extensions for the algebras in the quasivarieties of the equivalent algebraic semantics of the logics. Correspondence results for axiomatic extensions of the logics of implication that we study rely on a fully abstract translation into their modal companions and they are calculated using a generalized Sahlqvist - van Benthem algorithm.
HQPEF-Py: Metrics, Python Patterns, and Guidance for Evaluating Hybrid Quantum Programs
Venue: arXiv cs.SE 2025
Year: 2025

We study how to evaluate hybrid quantum programs as end-to-end workflows rather than as isolated devices or algorithms. Building on the Hybrid Quantum Program Evaluation Framework (HQPEF), we formalize a workflow-aware Quantum Readiness Level (QRL) score; define a normalized speedup under quality constraints for the Utility of Quantumness (UQ); and provide a timing-and-drift audit for hybrid pipelines. We complement these definitions with concise Python reference implementations that illustrate how to instantiate the metrics and audit procedures with state-of-the-art classical and quantum solvers (e.g., via Qiskit or PennyLane), while preserving matched-budget discipline and reproducibility.
Unified Deep Learning Platform for Dust and Fault Diagnosis in Solar Panels Using Thermal and Visual Imaging
Venue: arXiv cs.CV 2025
Year: 2025

Solar energy is one of the most abundant and tapped sources of renewable energies with enormous future potential. Solar panel output can vary widely with factors like intensity, temperature, dirt, debris and so on affecting it. We have implemented a model on detecting dust and fault on solar panels. These two applications are centralized as a single-platform and can be utilized for routine-maintenance and any other checks. These are checked against various parameters such as power output, sinusoidal wave (I-V component of solar cell), voltage across each solar cell and others. Firstly, we filter and preprocess the obtained images using gamma removal and Gaussian filtering methods alongside some predefined processes like normalization. The first application is to detect whether a solar cell is dusty or not based on various pre-determined metrics like shadowing, leaf, droppings, air pollution and from other human activities to extent of fine-granular solar modules. The other one is detecting faults and other such occurrences on solar panels like faults, cracks, cell malfunction using thermal imaging application. This centralized platform can be vital since solar panels have different efficiency across different geography (air and heat affect) and can also be utilized for small-scale house requirements to large-scale solar farm sustentation effectively. It incorporates CNN, ResNet models that with self-attention mechanisms-KerNet model which are used for classification and results in a fine-tuned system that detects dust or any fault occurring. Thus, this multi-application model proves to be efficient and optimized in detecting dust and faults on solar panels. We have performed various comparisons and findings that demonstrates that our model has better efficiency and accuracy results overall than existing models.
RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks (PINNs) typically minimize average residuals, which can conceal large, localized errors. We propose Residual Risk-Aware Physics-Informed Neural Networks PINNs (RRaPINNs), a single-network framework that optimizes tail-focused objectives using Conditional Value-at-Risk (CVaR), we also introduced a Mean-Excess (ME) surrogate penalty to directly control worst-case PDE residuals. This casts PINN training as risk-sensitive optimization and links it to chance-constrained formulations. The method is effective and simple to implement. Across several partial differential equations (PDEs) such as Burgers, Heat, Korteweg-de-Vries, and Poisson (including a Poisson interface problem with a source jump at x=0.5) equations, RRaPINNs reduce tail residuals while maintaining or improving mean errors compared to vanilla PINNs, Residual-Based Attention and its variant using convolution weighting; the ME surrogate yields smoother optimization than a direct CVaR hinge. The chance constraint reliability level $α$ acts as a transparent knob trading bulk accuracy (lower $α$ ) for stricter tail control (higher $α$ ). We discuss the framework limitations, including memoryless sampling, global-only tail budgeting, and residual-centric risk, and outline remedies via persistent hard-point replay, local risk budgets, and multi-objective risk over BC/IC terms. RRaPINNs offer a practical path to reliability-aware scientific ML for both smooth and discontinuous PDEs.
End-to-End Automated Logging via Multi-Agent Framework
Venue: arXiv cs.SE 2025
Year: 2025

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose Autologger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. Autologger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on what to log. These agents work together, utilizing our designed program analysis and retrieval tools. We evaluate Autologger on a large corpus from three mature open-source projects against state-of-the-art baselines. Our results show that Autologger achieves 96.63\% F1-score on the crucial whether-to-log decision. In an end-to-end setting, Autologger improves the overall quality of generated logging statements by 16.13\% over the strongest baseline, as measured by an LLM-as-a-judge score. We also demonstrate that our framework is generalizable, consistently boosting the performance of various backbone LLMs.
Emotion-Aware Conversational Recommender Systems: a Case Study
Venue: arXiv cs.HC 2025
Year: 2025

In recent years, online shopping has grown rapidly, especially during the COVID-19 period. However, it still lacks elements typical of physical stores, such as empathic support and personalised advice from a sales assistant. This study explores how an emotion-aware Conversational Agent (CA) can improve the online shopping experience by responding to user emotions in a more natural and human way. The project focuses on Gala, a virtual assistant developed for the Galeries Lafayette website, capable of recognising emotional states from voice messages and adapting its responses accordingly. User needs were first analysed through semi-structured interviews, which informed the design of Gala's UX and functionalities. Gala was implemented using the OpenAI API and the Galeries Lafayette API, adopting a Content-Based recommendation approach. Through Natural Language Processing, it interprets user requests and retrieves products aligned with specific attributes such as name, price, and brand, enabling fluid dialogue and tailored suggestions. Two user studies were conducted: a usability test and a comparative evaluation between a standard CA and Gala's emotion-aware version. The results highlight the potential of emotion-aware CAs to make online shopping faster, more engaging, and closer to an in-store guided experience.
Dealing with the Hard Facts of Low-Resource African NLP
Venue: arXiv cs.CL 2025
Year: 2025

Creating speech datasets, models, and evaluation frameworks for low-resource languages remains challenging given the lack of a broad base of pertinent experience to draw from. This paper reports on the field collection of 612 hours of spontaneous speech in Bambara, a low-resource West African language; the semi-automated annotation of that dataset with transcriptions; the creation of several monolingual ultra-compact and small models using the dataset; and the automatic and human evaluation of their output. We offer practical suggestions for data collection protocols, annotation, and model design, as well as evidence for the importance of performing human evaluation. In addition to the main dataset, multiple evaluation datasets, models, and code are made publicly available.